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        <title>On-Device Text Representations Robust To Misspellings via Projections</title>
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        <namePart type="given">Chinnadhurai</namePart>
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    <abstract>Recently, there has been a strong interest in developing natural language applications that live on personal devices such as mobile phones, watches and IoT with the objective to preserve user privacy and have low memory. Advances in Locality-Sensitive Hashing (LSH)-based projection networks have demonstrated state-of-the-art performance in various classification tasks without explicit word (or word-piece) embedding lookup tables by computing on-the-fly text representations. In this paper, we show that the projection based neural classifiers are inherently robust to misspellings and perturbations of the input text. We empirically demonstrate that the LSH projection based classifiers are more robust to common misspellings compared to BiLSTMs (with both word-piece &amp; word-only tokenization) and fine-tuned BERT based methods. When subject to misspelling attacks, LSH projection based classifiers had a small average accuracy drop of 2.94% across multiple classifications tasks, while the fine-tuned BERT model accuracy had a significant drop of 11.44%.</abstract>
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        <date>2021-04</date>
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%0 Conference Proceedings
%T On-Device Text Representations Robust To Misspellings via Projections
%A Sankar, Chinnadhurai
%A Ravi, Sujith
%A Kozareva, Zornitsa
%Y Merlo, Paola
%Y Tiedemann, Jorg
%Y Tsarfaty, Reut
%S Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume
%D 2021
%8 April
%I Association for Computational Linguistics
%C Online
%F sankar-etal-2021-device
%X Recently, there has been a strong interest in developing natural language applications that live on personal devices such as mobile phones, watches and IoT with the objective to preserve user privacy and have low memory. Advances in Locality-Sensitive Hashing (LSH)-based projection networks have demonstrated state-of-the-art performance in various classification tasks without explicit word (or word-piece) embedding lookup tables by computing on-the-fly text representations. In this paper, we show that the projection based neural classifiers are inherently robust to misspellings and perturbations of the input text. We empirically demonstrate that the LSH projection based classifiers are more robust to common misspellings compared to BiLSTMs (with both word-piece & word-only tokenization) and fine-tuned BERT based methods. When subject to misspelling attacks, LSH projection based classifiers had a small average accuracy drop of 2.94% across multiple classifications tasks, while the fine-tuned BERT model accuracy had a significant drop of 11.44%.
%R 10.18653/v1/2021.eacl-main.250
%U https://aclanthology.org/2021.eacl-main.250
%U https://doi.org/10.18653/v1/2021.eacl-main.250
%P 2871-2876

Markdown (Informal)

[On-Device Text Representations Robust To Misspellings via Projections](https://aclanthology.org/2021.eacl-main.250) (Sankar et al., EACL 2021)

• On-Device Text Representations Robust To Misspellings via Projections (Sankar et al., EACL 2021)

ACL

• Chinnadhurai Sankar, Sujith Ravi, and Zornitsa Kozareva. 2021. On-Device Text Representations Robust To Misspellings via Projections. In Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pages 2871–2876, Online. Association for Computational Linguistics.